Enhanced VR Experience With Edge Computing: The Impact of Decoding Latency

Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences. 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novel <u>l</u>atency-<u>a</u>ware <u>q</u>uality <u>a</u>daptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm, respectively, in terms of QoE at 4K resolution. Furthermore, LAQA ensures excellent scalability by simulating the number of users ranging from 15 to 60, making it a robust solution for diverse and growing user scales.